Skill UI

This skill systematically reviews test code to identify and correct common anti-patterns. It detects issues such as flaky tests, overly mocked dependencies (over-mocking), brittle assertions, excessive test size (The Giant), and structural dependencies. It explains the flaw, provides best practices, and suggests refactored, reliable implementations, helping maintain high test quality and stability.

A comprehensive scaffold generator for building Model Context Protocol (MCP) servers using TypeScript and Node.js. This tool provides a complete, production-ready boilerplate, including proper project structure, dependency management (e.g., @modelcontextprotocol/sdk, zod), and implementation details for tools and resources. It supports both HTTP and stdio transport mechanisms, ensuring type safety, robust error handling, and best practices for modern backend development.

Manage StyleSeed design tokens across JSON and CSS sources, adding or updating colors, spacing, typography, and other tokens while keeping implementations and dark mode consistent for teams working with Toss seed projects.

A comprehensive guide for writing and reviewing robust, behavior-first unit tests for Vue 3, TypeScript, and Pinia stores. This skill covers advanced patterns for testing components, composables, and state management using Vitest and Vue Test Utils, emphasizing black-box assertions and minimizing implementation coupling.

Automatically update implementation plan files to reflect new requirements, ensuring structured, machine-readable phases with explicit tasks and validation criteria for AI agents to execute deterministically.

Implements verification gates as explicit phase checkpoints, producing acceptance checklists, CI gate configs, and manual approval rules so teams catch defects before phase transitions, covering requirements, design, implementation, integration, and deployment.

This structured prompt guides an AI model to proactively analyze a user's question and systematically list all necessary files for accurate answering. It forces the AI to categorize required context into 'Must See' (critical), 'Should See' (helpful), 'Already Have,' and 'Uncertainties.' Ideal for complex coding tasks, debugging, or feature implementation that spans multiple codebase files.

This skill guides the systematic process of converting vague ideas or initial requirements into complete, detailed, and approved technical specifications. It enforces a structured workflow, including context exploration, iterative questioning, proposing multiple options with trade-offs, and creating documented blueprints. Critically, it ensures that no implementation or code writing occurs until the design has been fully vetted and formally approved by the user.

A comprehensive, four-stage methodology for debugging complex software issues, ensuring that fixes target the root cause rather than merely masking symptoms. This guide mandates rigorous investigation (gathering evidence, tracing data flow), pattern analysis, controlled hypothesis testing, and disciplined implementation via test-driven development. Use this when facing production bugs, test failures, performance degradation, or unexplained system behavior, especially under time pressure.

This skill analyzes the implementation gap by comparing approved feature requirements against the existing codebase, architecture, and context. It helps identify missing capabilities, potential integration challenges, and necessary technical research areas. The output provides multiple viable implementation options and a comprehensive analysis report, guiding the transition from requirements definition to technical design.

Stable Baselines3 is a robust, PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms (PPO, SAC, DQN, etc.). It facilitates the training of single-agent RL models, supports the creation of custom Gymnasium environments, and handles advanced parallel training via vectorized environments. It is ideal for rapid prototyping and rigorous RL experiments.

This skill generates comprehensive and structured requirements documents for a new specification. It systematically aggregates context from project files, business rules, and technical constraints, ensuring all generated requirements focus on user-observable behavior (WHAT) rather than implementation details (HOW). It follows established standards like EARS format and includes a rigorous review gate to ensure scope clarity and completeness.